dmvnorm_rcpp

C++ implementation of multivariate Normal probability density function for multiple inputs

Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.

Brett T McClintock

momentuHMM

Maximum Likelihood Analysis of Animal Movement Behavior Using
Multivariate Hidden Markov Models

Brett McClintock

Theo Michelot

dmvnorm_rcpp function

<dl><dt>x</dt>
<dd>data matrix of dimension <code>p x n</code>, <code>p</code> being the dimension of the
data and n the number of data points.</dd>
<dt>mean</dt>
<dd>mean vectors matrix of dimension <code>p x n</code></dd>
<dt>varcovM</dt>
<dd>list of length <code>n</code> of variance-covariance matrices,
each of dimensions <code>p x p</code>.</dd></dl>

Arguments

C++ implementation of multivariate Normal probability density function for multiple inputs — dmvnorm_rcpp

<dl>

<dt>x</dt>
<dd>data matrix of dimension <code>p x n</code>, <code>p</code> being the dimension of the
data and n the number of data points.</dd>


<dt>mean</dt>
<dd>mean vectors matrix of dimension <code>p x n</code></dd>


<dt>varcovM</dt>
<dd>list of length <code>n</code> of variance-covariance matrices,
each of dimensions <code>p x p</code>.</dd>

</dl>

dmvnorm_rcpp: C++ implementation of multivariate Normal probability density function for multiple inputs

Description

Usage

Value

Arguments